Academic Journal
Comparative RSM-ML analysis to predict cellulase production from Lysinibacillus capsici isolate using banana pseudostem as a sustainable substrate.
| Τίτλος: | Comparative RSM-ML analysis to predict cellulase production from Lysinibacillus capsici isolate using banana pseudostem as a sustainable substrate. |
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| Συγγραφείς: | Lilaria K; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India., Tiwari S; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India. Electronic address: shubhratiwari77@gmail.com., Jadhav SK; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India. |
| Πηγή: | International journal of biological macromolecules [Int J Biol Macromol] 2026 Jun; Vol. 368, pp. 152665. Date of Electronic Publication: 2026 May 23. |
| Τύπος έκδοσης: | Journal Article; Comparative Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 7909578 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0003 (Electronic) Linking ISSN: 01418130 NLM ISO Abbreviation: Int J Biol Macromol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Amsterdam : Elsevier Original Publication: Guildford, Eng., IPC Science and Technology Press. |
| Ιατρικοί όροι (MeSH): | Musa*/chemistry , Musa*/microbiology , Cellulase*/biosynthesis , Bacillaceae*/enzymology , Bacillaceae*/isolation & purification , Bacillaceae*/genetics , Machine Learning*, RNA, Ribosomal, 16S/genetics ; Neural Networks, Computer ; Support Vector Machine ; Hydrogen-Ion Concentration ; Temperature ; Bayes Theorem |
| Περίληψη: | A key bottleneck in the sustainable valorization of lignocellulosic biomass remains the efficient, low-cost production of cellulase. Although widely used, Response Surface Methodology (RSM) is limited by its quadratic assumptions, which may not capture nonlinear interactions. Despite progress in using Machine Learning (ML) for cellulase prediction, integrated approaches that combine RSM with interpretable ML methods, along with regression diagnostics, permutation importance, and SHAP-inspired (Shapley Additive Explanations) methods, remain exceedingly scarce in cellulase studies, limiting both predictive performance and biological insight. In this work, a cellulolytic bacterium was isolated from the banana pseudostem and identified as Lysinibacillus capsici through 16S rRNA gene analysis. Box-Behnken Design (BBD) was adopted to optimize cellulase production, considering key process parameters such as pH, temperature, incubation time, substrate concentration, and inoculum volume. To improve predictive capability, a comparative study was conducted on four ML techniques: Artificial Neural Network (ANN), Bayesian Regularized Neural Network (BRNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). Among the evaluated models, BRNN and RBFNN showed slightly better performance; however, the differences were marginal, indicating comparable overall predictive behavior. Regression plots and statistical metrics validated model robustness, while SHAP-inspired analysis quantified the relative contributions of substrate concentration, pH, and temperature. The findings emphasize that predictive performance is influenced by both the modeling strategy and data availability; therefore, comparisons between RSM and machine learning models should be interpreted in this context. Overall, this integrated RSM-ML framework provides a structured and interpretable approach for optimizing enzyme production from lignocellulosic biomass. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. |
| Contributed Indexing: | Keywords: Banana Pseudostem; Cellulase; Lysinibacillus capsici; Machine learning; Response surface methodology; SHAP-inspired analysis; Support vector machine |
| Substance Nomenclature: | EC 3.2.1.4 (Cellulase) 0 (RNA, Ribosomal, 16S) |
| Entry Date(s): | Date Created: 20260523 Date Completed: 20260611 Latest Revision: 20260611 |
| Update Code: | 20260611 |
| DOI: | 10.1016/j.ijbiomac.2026.152665 |
| PMID: | 42176927 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-0003 |
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| DOI: | 10.1016/j.ijbiomac.2026.152665 |